Feature/graph-coloring-example #389
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New OpenEvolve example demonstrating evolution of graph coloring algorithms: - Simple greedy coloring as initial program with EVOLVE-BLOCK - Evaluator testing on Petersen, K5, bipartite, cycle, and random graphs - Visualization support with networkx/matplotlib - Custom graph file loading with sample graphs - 26 unit tests covering coloring validity and file loading Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
…ring - Implement 3-stage cascade evaluation: - Stage 1: Validity gate (quick pass/fail on 3 small graphs) - Stage 2: Quick scoring on small graphs with chromatic numbers - Stage 3: Full evaluation on all 6 test graphs - Enforce combined_score = 0 for any invalid coloring - Add TestEvaluatorFailureCases class with 10 mock program tests: - Invalid coloring, partial coloring, suboptimal coloring - Missing functions, syntax errors, runtime errors - Exception handling for all stages - Update config.yaml with cascade threshold documentation - Total test count: 50 tests (40 existing + 10 new failure cases) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add Crown, Mycielski, Chvátal graphs that trip up greedy/DSatur - Add DSatur-adversarial random graphs (seeds 19, 70) - Archive Gemini run (score 0.9667) and Claude manual evolution (score 1.0) - Switch config from Gemini to Claude API Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement quadratic time budget formula that scales with graph complexity: - budget = BASE_TIME_BUDGET + COEFF_TIME × (n² + m) - BASE_TIME_BUDGET = 1ms, COEFF_TIME = 8.8e-7 seconds - TIME_PENALTY_WEIGHT = 0.3 (30% of combined score) - Time score decays as 1/overage_ratio with floor at 0.1 This penalizes brute-force multi-start algorithms while allowing efficient single-pass algorithms to pass comfortably. Add 12 unit tests for time budget functionality covering: - Formula correctness and quadratic scaling - Time score calculation and decay behavior - Integration with evaluate() function Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add DIMACS .col file parser with 1-indexed to 0-indexed conversion - Create benchmarks directory with 22 full and 5 small benchmark graphs - Add chromatic numbers registry JSON for known optimal values - Update evaluator to use DIMACS benchmarks when available - Add fallback to built-in graphs if benchmarks not present - Handle unknown chromatic numbers with max_degree/2 heuristic - Add 15 unit tests for DIMACS functionality - Archive previous Claude OpenEvolve run results Benchmark includes challenging graphs: queen (n=5-13), myciel, le450_15c, mulsol, zeroin, inithx - current DSatur scores 0.916. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add archived experiment run_20260202_claude_openevolve_dimacs with: - DSatur initial program, evolved best program, config, and run notes - Key finding: post-processing is dead code (0 recolorings) - Improvement came from starting with highest-degree vertex - Add analyze_postprocessing.py script for analyzing recoloring effectiveness Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Co-Authored-By: Claude Opus 4.5 noreply@anthropic.com